1. Field of the Invention
The present invention relates to a method for recovering target speech based on speech segment detection under a stationary noise by extracting signal components falling in a speech segment, which is determined based on separated signals obtained through the Independent Component Analysis (ICA), thereby minimizing the residual noise in the recovered target speech.
2. Description of the Related Art
Recently the speech recognition technology has significantly improved and achieved provision of speech recognition engines with extremely high recognition capabilities for the case of ideal environments, i.e. no surrounding noises. However, it is still difficult to attain a desirable recognition rate in a household environment or offices where there are sounds of daily activities and the like. In order to take advantage of the inherent capability of the speech recognition engine in such environments, pre-processing is needed to remove noises from the mixed signals and pass only the target speech such as a speaker's speech to the engine.
In this respect, the ICA and other speech emphasizing methods have been widely utilized and various algorithms have been proposed. (For example, see the following five references: 1. “An Information Maximization Approach to Blind Separation and Blind Deconvolution”, by J. Bell and T. J. Sejnowski, Neural Computation, USA, MIT Press, Jun. 1995, Vol. 7, No. 6, pp 1129-1159; 2. “Natural Gradient Works Efficiently in Learning”, by S. Amari, Neural Computation, USA, MIT Press, February 1998, Vol. 10, No. 2, pp. 254-276; 3.“Independent Component Analysis Using an Extended Informax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources”, by T. W. Lee, M. Girolami, and T. J. Sejnowski, Neural Computation, USA, MIT Press, February 1999, Vol. 11, No. 2, pp. 417-441; 4. “Fast and Robust Fixed-Point Algorithms for Independent Component Analysis”, by A Hyvarinen, IEEE Trans. Neural Networks, USA, IEEE, June 1999, Vol. 10, No. 3, pp. 626-634; and 5. “Independent Component Analysis: Algorithms and Applications”, by A. Hyvarinen and E. Oja, Neural Networks, USA, Pergamon Press, June 2000, Vol. 13, No. 4-5, pp. 411-430.) Among various algorithms, the ICA is a method for separating noises from speech on the assumption that the sound sources are statistically independent.
Although the ICA is capable of separating noises from speech well under ideal conditions without reverberation, its separation ability greatly degrades under real-life conditions with strong reverberation due to residual noises caused by the reverberation.